Non-uniform quantized feedback in federated learning

ABSTRACT

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client device may determine a feedback associated with a machine learning component based at least in part on applying the machine learning component. Accordingly, the client device may transmit a quantized value based at least in part on the feedback. The quantized value is determined based at least in part on distances between the feedback and a non-uniform set of quantized digits. Numerous other aspects are provided.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application claims priority to U.S. Provisional PatentApplication No. 63/085,743, filed on Sep. 30, 2020, entitled“NON-UNIFORM QUANTIZED FEEDBACK IN FEDERATED LEARNING,” and assigned tothe assignee hereof. The disclosure of the prior application isconsidered part of and is incorporated by reference in this patentapplication.

FIELD OF THE DISCLOSURE

Aspects of the present disclosure generally relate to wirelesscommunication and to techniques and apparatuses for transmitting andreceiving non-uniform quantized feedback in federated learning.

BACKGROUND

Wireless communication systems are widely deployed to provide varioustelecommunication services such as telephony, video, data, messaging,and broadcasts. Typical wireless communication systems may employmultiple-access technologies capable of supporting communication withmultiple users by sharing available system resources (e.g., bandwidth,transmit power, or the like). Examples of such multiple-accesstechnologies include code division multiple access (CDMA) systems, timedivision multiple access (TDMA) systems, frequency division multipleaccess (FDMA) systems, orthogonal frequency division multiple access(OFDMA) systems, single-carrier frequency division multiple access(SC-FDMA) systems, time division synchronous code division multipleaccess (TD-SCDMA) systems, and Long Term Evolution (LTE).LTE/LTE-Advanced is a set of enhancements to the Universal MobileTelecommunications System (UMTS) mobile standard promulgated by theThird Generation Partnership Project (3GPP).

A wireless network may include one or more base stations that supportcommunication for a user equipment (UE) or multiple UEs. A UE maycommunicate with a base station via downlink communications and uplinkcommunications. “Downlink” (or “DL”) refers to a communication link fromthe base station to the UE, and “uplink” (or “UL”) refers to acommunication link from the UE to the base station.

The above multiple access technologies have been adopted in varioustelecommunication standards to provide a common protocol that enablesdifferent UEs to communicate on a municipal, national, regional, and/orglobal level. New Radio (NR), which may be referred to as 5G, is a setof enhancements to the LTE mobile standard promulgated by the 3GPP. NRis designed to better support mobile broadband internet access byimproving spectral efficiency, lowering costs, improving services,making use of new spectrum, and better integrating with other openstandards using orthogonal frequency division multiplexing (OFDM) with acyclic prefix (CP) (CP-OFDM) on the downlink, using CP-OFDM and/orsingle-carrier frequency division multiplexing (SC-FDM) (also known asdiscrete Fourier transform spread OFDM (DFT-s-OFDM)) on the uplink, aswell as supporting beamforming, multiple-input multiple-output (MIMO)antenna technology, and carrier aggregation. As the demand for mobilebroadband access continues to increase, further improvements in LTE, NR,and other radio access technologies remain useful.

SUMMARY

Some aspects described herein relate to a method of wirelesscommunication performed by a client device. The method may includedetermining a feedback associated with a machine learning componentbased at least in part on applying the machine learning component. Themethod may further include transmitting a quantized value based at leastin part on the feedback, wherein the quantized value is determined basedat least in part on distances between the feedback and a non-uniform setof quantized digits.

Some aspects described herein relate to a method of wirelesscommunication performed by a server device. The method may includetransmitting, to a client device, a configuration associated with amachine learning component, wherein the machine learning componentaccepts one or more inputs to generate one or more outputs. The methodmay further include receiving a quantized value based at least in parton feedback from the client device having applied the machine learningcomponent, wherein the quantized value is based at least in part ondistances between the feedback and a non-uniform set of quantizeddigits.

Some aspects described herein relate to an apparatus for wirelesscommunication at a client device. The client device may include a memoryand one or more processors coupled to the memory. The one or moreprocessors may be configured to determine a feedback associated with amachine learning component based at least in part on applying themachine learning component. The one or more processors may be furtherconfigured to transmit a quantized value based at least in part on thefeedback, wherein the quantized value is determined based at least inpart on distances between the feedback and a non-uniform set ofquantized digits.

Some aspects described herein relate to an apparatus for wirelesscommunication at a server device. The server device may include a memoryand one or more processors coupled to the memory. The one or moreprocessors may be configured to transmit, to a client device, aconfiguration associated with a machine learning component, wherein themachine learning component accepts one or more inputs to generate one ormore outputs. The one or more processors may be further configured toreceive a quantized value based at least in part on feedback from theclient device having applied the machine learning component, wherein thequantized value is based at least in part on distances between thefeedback and a non-uniform set of quantized digits.

Some aspects described herein relate to a non-transitorycomputer-readable medium storing a set of instructions for wirelesscommunication. The one or more instructions, when executed by one ormore processors of a client device, may cause the client device todetermine a feedback associated with a machine learning component basedat least in part on applying the machine learning component. The one ormore instructions, when executed by one or more processors of a clientdevice, may further cause the client device to transmit a quantizedvalue based at least in part on the feedback, wherein the quantizedvalue is determined based at least in part on distances between thefeedback and a non-uniform set of quantized digits.

Some aspects described herein relate to a non-transitorycomputer-readable medium storing a set of instructions for wirelesscommunication. The one or more instructions, when executed by one ormore processors of a server device, may cause the server device totransmit, to a client device, a configuration associated with a machinelearning component, wherein the machine learning component accepts oneor more inputs to generate one or more outputs. The one or moreinstructions, when executed by one or more processors of a serverdevice, may further cause the server device to receive a quantized valuebased at least in part on feedback from the client device having appliedthe machine learning component, wherein the quantized value is based atleast in part on distances between the feedback and a non-uniform set ofquantized digits.

Some aspects described herein relate to an apparatus for wirelesscommunication. The apparatus may include means for determining afeedback associated with a machine learning component based at least inpart on applying the machine learning component. The apparatus mayfurther include means for transmitting a quantized value based at leastin part on the feedback, wherein the quantized value is determined basedat least in part on distances between the feedback and a non-uniform setof quantized digits.

Some aspects described herein relate to an apparatus for wirelesscommunication. The apparatus may include means for transmitting, to aclient device, a configuration associated with a machine learningcomponent, wherein the machine learning component accepts one or moreinputs to generate one or more outputs. The apparatus may furtherinclude means for receiving a quantized value based at least in part onfeedback from the client device having applied the machine learningcomponent, wherein the quantized value is based at least in part ondistances between the feedback and a non-uniform set of quantizeddigits.

Aspects generally include a method, apparatus, system, computer programproduct, non-transitory computer-readable medium, user equipment, basestation, wireless communication device, and/or processing system assubstantially described herein with reference to and as illustrated bythe drawings and specification.

The foregoing has outlined rather broadly the features and technicaladvantages of examples according to the disclosure in order that thedetailed description that follows may be better understood. Additionalfeatures and advantages will be described hereinafter. The conceptionand specific examples disclosed may be readily utilized as a basis formodifying or designing other structures for carrying out the samepurposes of the present disclosure. Such equivalent constructions do notdepart from the scope of the appended claims. Characteristics of theconcepts disclosed herein, both their organization and method ofoperation, together with associated advantages, will be betterunderstood from the following description when considered in connectionwith the accompanying figures. Each of the figures is provided for thepurposes of illustration and description, and not as a definition of thelimits of the claims.

While aspects are described in the present disclosure by illustration tosome examples, those skilled in the art will understand that suchaspects may be implemented in many different arrangements and scenarios.Techniques described herein may be implemented using different platformtypes, devices, systems, shapes, sizes, and/or packaging arrangements.For example, some aspects may be implemented via integrated chipembodiments or other non-module-component based devices (e.g., end-userdevices, vehicles, communication devices, computing devices, industrialequipment, retail/purchasing devices, medical devices, and/or artificialintelligence devices). Aspects may be implemented in chip-levelcomponents, modular components, non-modular components, non-chip-levelcomponents, device-level components, and/or system-level components.Devices incorporating described aspects and features may includeadditional components and features for implementation and practice ofclaimed and described aspects. For example, transmission and receptionof wireless signals may include one or more components for analog anddigital purposes (e.g., hardware components including antennas, radiofrequency (RF) chains, power amplifiers, modulators, buffers,processors, interleavers, adders, and/or summers). It is intended thataspects described herein may be practiced in a wide variety of devices,components, systems, distributed arrangements, and/or end-user devicesof varying size, shape, and constitution.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the above-recited features of the present disclosure can beunderstood in detail, a more particular description, briefly summarizedabove, may be had by reference to aspects, some of which are illustratedin the appended drawings. It is to be noted, however, that the appendeddrawings illustrate only certain typical aspects of this disclosure andare therefore not to be considered limiting of its scope, for thedescription may admit to other equally effective aspects. The samereference numbers in different drawings may identify the same or similarelements.

FIG. 1 is a diagram illustrating an example of a wireless network, inaccordance with the present disclosure.

FIG. 2 is a diagram illustrating an example of a base station incommunication with a user equipment (UE) in a wireless network, inaccordance with the present disclosure.

FIG. 3 is a diagram illustrating an example of federated learning formachine learning components, in accordance with the present disclosure.

FIG. 4 is a diagram illustrating an example associated with non-uniformquantization, in accordance with the present disclosure.

FIG. 5 is a diagram illustrating an example associated with transmittingand receiving non-uniform quantized feedback in federated learning, inaccordance with the present disclosure.

FIGS. 6 and 7 are diagrams illustrating example processes associatedwith transmitting and receiving non-uniform quantized feedback infederated learning, in accordance with the present disclosure.

FIGS. 8 and 9 are diagrams of example apparatuses for wirelesscommunication, in accordance with the present disclosure.

DETAILED DESCRIPTION

Various aspects of the disclosure are described more fully hereinafterwith reference to the accompanying drawings. This disclosure may,however, be embodied in many different forms and should not be construedas limited to any specific structure or function presented throughoutthis disclosure. Rather, these aspects are provided so that thisdisclosure will be thorough and complete, and will fully convey thescope of the disclosure to those skilled in the art. One skilled in theart should appreciate that the scope of the disclosure is intended tocover any aspect of the disclosure disclosed herein, whether implementedindependently of or combined with any other aspect of the disclosure.For example, an apparatus may be implemented or a method may bepracticed using any number of the aspects set forth herein. In addition,the scope of the disclosure is intended to cover such an apparatus ormethod which is practiced using other structure, functionality, orstructure and functionality in addition to or other than the variousaspects of the disclosure set forth herein. It should be understood thatany aspect of the disclosure disclosed herein may be embodied by one ormore elements of a claim.

Several aspects of telecommunication systems will now be presented withreference to various apparatuses and techniques. These apparatuses andtechniques will be described in the following detailed description andillustrated in the accompanying drawings by various blocks, modules,components, circuits, steps, processes, algorithms, or the like(collectively referred to as “elements”). These elements may beimplemented using hardware, software, or combinations thereof. Whethersuch elements are implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem.

While aspects may be described herein using terminology commonlyassociated with a 5G or New Radio (NR) radio access technology (RAT),aspects of the present disclosure can be applied to other RATs, such asa 3G RAT, a 4G RAT, and/or a RAT subsequent to 5G (e.g., 6G).

FIG. 1 is a diagram illustrating an example of a wireless network 100,in accordance with the present disclosure. The wireless network 100 maybe or may include elements of a 5G (e.g., NR) network and/or a 4G (e.g.,Long Term Evolution (LTE)) network, among other examples. The wirelessnetwork 100 may include one or more base stations 110 (shown as a BS 110a, a BS 110 b, a BS 110 c, and a BS 110 d), a user equipment (UE) 120 ormultiple UEs 120 (shown as a UE 120 a, a UE 120 b, a UE 120 c, a UE 120d, and a UE 120 e), and/or other network entities. A base station 110 isan entity that communicates with UEs 120. A base station 110 (sometimesreferred to as a BS) may include, for example, an NR base station, anLTE base station, a Node B, an eNB (e.g., in 4G), a gNB (e.g., in 5G),an access point, and/or a transmission reception point (TRP). Each basestation 110 may provide communication coverage for a particulargeographic area. In the Third Generation Partnership Project (3GPP), theterm “cell” can refer to a coverage area of a base station 110 and/or abase station subsystem serving this coverage area, depending on thecontext in which the term is used.

A base station 110 may provide communication coverage for a macro cell,a pico cell, a femto cell, and/or another type of cell. A macro cell maycover a relatively large geographic area (e.g., several kilometers inradius) and may allow unrestricted access by UEs 120 with servicesubscriptions. A pico cell may cover a relatively small geographic areaand may allow unrestricted access by UEs 120 with service subscription.A femto cell may cover a relatively small geographic area (e.g., a home)and may allow restricted access by UEs 120 having association with thefemto cell (e.g., UEs 120 in a closed subscriber group (CSG)). A basestation 110 for a macro cell may be referred to as a macro base station.A base station 110 for a pico cell may be referred to as a pico basestation. A base station 110 for a femto cell may be referred to as afemto base station or an in-home base station. In the example shown inFIG. 1 , the BS 110 a may be a macro base station for a macro cell 102a, the BS 110 b may be a pico base station for a pico cell 102 b, andthe BS 110 c may be a femto base station for a femto cell 102 c. A basestation may support one or multiple (e.g., three) cells.

In some examples, a cell may not necessarily be stationary, and thegeographic area of the cell may move according to the location of a basestation 110 that is mobile (e.g., a mobile base station). In someexamples, the base stations 110 may be interconnected to one anotherand/or to one or more other base stations 110 or network nodes (notshown) in the wireless network 100 through various types of backhaulinterfaces, such as a direct physical connection or a virtual network,using any suitable transport network.

The wireless network 100 may include one or more relay stations. A relaystation is an entity that can receive a transmission of data from anupstream station (e.g., a base station 110 or a UE 120) and send atransmission of the data to a downstream station (e.g., a UE 120 or abase station 110). A relay station may be a UE 120 that can relaytransmissions for other UEs 120. In the example shown in FIG. 1 , the BS110 d (e.g., a relay base station) may communicate with the BS 110 a(e.g., a macro base station) and the UE 120 d in order to facilitatecommunication between the BS 110 a and the UE 120 d. A base station 110that relays communications may be referred to as a relay station, arelay base station, a relay, or the like.

The wireless network 100 may be a heterogeneous network that includesbase stations 110 of different types, such as macro base stations, picobase stations, femto base stations, relay base stations, or the like.These different types of base stations 110 may have different transmitpower levels, different coverage areas, and/or different impacts oninterference in the wireless network 100. For example, macro basestations may have a high transmit power level (e.g., 5 to 40 watts)whereas pico base stations, femto base stations, and relay base stationsmay have lower transmit power levels (e.g., 0.1 to 2 watts).

A network controller 130 may couple to or communicate with a set of basestations 110 and may provide coordination and control for these basestations 110. The network controller 130 may communicate with the basestations 110 via a backhaul communication link. The base stations 110may communicate with one another directly or indirectly via a wirelessor wireline backhaul communication link.

The UEs 120 may be dispersed throughout the wireless network 100, andeach UE 120 may be stationary or mobile. A UE 120 may include, forexample, an access terminal, a terminal, a mobile station, and/or asubscriber unit. A UE 120 may be a cellular phone (e.g., a smart phone),a personal digital assistant (PDA), a wireless modem, a wirelesscommunication device, a handheld device, a laptop computer, a cordlessphone, a wireless local loop (WLL) station, a tablet, a camera, a gamingdevice, a netbook, a smartbook, an ultrabook, a medical device, abiometric device, a wearable device (e.g., a smart watch, smartclothing, smart glasses, a smart wristband, smart jewelry (e.g., a smartring or a smart bracelet)), an entertainment device (e.g., a musicdevice, a video device, and/or a satellite radio), a vehicular componentor sensor, a smart meter/sensor, industrial manufacturing equipment, aglobal positioning system device, and/or any other suitable device thatis configured to communicate via a wireless medium.

Some UEs 120 may be considered machine-type communication (MTC) orevolved or enhanced machine-type communication (eMTC) UEs. An MTC UEand/or an eMTC UE may include, for example, a robot, a drone, a remotedevice, a sensor, a meter, a monitor, and/or a location tag, that maycommunicate with a base station, another device (e.g., a remote device),or some other entity. Some UEs 120 may be considered Internet-of-Things(IoT) devices, and/or may be implemented as NB-IoT (narrowband IoT)devices. Some UEs 120 may be considered a Customer Premises Equipment. AUE 120 may be included inside a housing that houses components of the UE120, such as processor components and/or memory components. In someexamples, the processor components and the memory components may becoupled together. For example, the processor components (e.g., one ormore processors) and the memory components (e.g., a memory) may beoperatively coupled, communicatively coupled, electronically coupled,and/or electrically coupled.

In general, any number of wireless networks 100 may be deployed in agiven geographic area. Each wireless network 100 may support aparticular RAT and may operate on one or more frequencies. A RAT may bereferred to as a radio technology, an air interface, or the like. Afrequency may be referred to as a carrier, a frequency channel, or thelike. Each frequency may support a single RAT in a given geographic areain order to avoid interference between wireless networks of differentRATs. In some cases, NR or 5G RAT networks may be deployed.

In some examples, two or more UEs 120 (e.g., shown as UE 120 a and UE120 e) may communicate directly using one or more sidelink channels(e.g., without using a base station 110 as an intermediary tocommunicate with one another). For example, the UEs 120 may communicateusing peer-to-peer (P2P) communications, device-to-device (D2D)communications, a vehicle-to-everything (V2X) protocol (e.g., which mayinclude a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure(V2I) protocol, or a vehicle-to-pedestrian (V2P) protocol), and/or amesh network. In such examples, a UE 120 may perform schedulingoperations, resource selection operations, and/or other operationsdescribed elsewhere herein as being performed by the base station 110.

Devices of the wireless network 100 may communicate using theelectromagnetic spectrum, which may be subdivided by frequency orwavelength into various classes, bands, channels, or the like. Forexample, devices of the wireless network 100 may communicate using oneor more operating bands. In 5G NR, two initial operating bands have beenidentified as frequency range designations FR1 (410 MHz-7.125 GHz) andFR2 (24.25 GHz-52.6 GHz). It should be understood that although aportion of FR1 is greater than 6 GHz, FR1 is often referred to(interchangeably) as a “Sub-6 GHz” band in various documents andarticles. A similar nomenclature issue sometimes occurs with regard toFR2, which is often referred to (interchangeably) as a “millimeter wave”band in documents and articles, despite being different from theextremely high frequency (EHF) band (30 GHz-300 GHz) which is identifiedby the International Telecommunications Union (ITU) as a “millimeterwave” band.

The frequencies between FR1 and FR2 are often referred to as mid-bandfrequencies. Recent 5G NR studies have identified an operating band forthese mid-band frequencies as frequency range designation FR3 (7.125GHz-24.25 GHz). Frequency bands falling within FR3 may inherit FR1characteristics and/or FR2 characteristics, and thus may effectivelyextend features of FR1 and/or FR2 into mid-band frequencies. Inaddition, higher frequency bands are currently being explored to extend5G NR operation beyond 52.6 GHz. For example, three higher operatingbands have been identified as frequency range designations FR4a or FR4-1(52.6 GHz-71 GHz), FR4 (52.6 GHz-114.25 GHz), and FR5 (114.25 GHz-300GHz). Each of these higher frequency bands falls within the EHF band.

With the above examples in mind, unless specifically stated otherwise,it should be understood that the term “sub-6 GHz” or the like, if usedherein, may broadly represent frequencies that may be less than 6 GHz,may be within FR1, or may include mid-band frequencies. Further, unlessspecifically stated otherwise, it should be understood that the term“millimeter wave” or the like, if used herein, may broadly representfrequencies that may include mid-band frequencies, may be within FR2,FR4, FR4-a or FR4-1, and/or FR5, or may be within the EHF band. It iscontemplated that the frequencies included in these operating bands(e.g., FR1, FR2, FR3, FR4, FR4-a, FR4-1, and/or FR5) may be modified,and techniques described herein are applicable to those modifiedfrequency ranges.

As indicated above, FIG. 1 is provided as an example. Other examples maydiffer from what is described with regard to FIG. 1 .

FIG. 2 is a diagram illustrating an example 200 of a base station 110 incommunication with a UE 120 in a wireless network 100, in accordancewith the present disclosure. The base station 110 may be equipped with aset of antennas 234 a through 234 t, such as T antennas (T≥1). The UE120 may be equipped with a set of antennas 252 a through 252 r, such asR antennas (R≥1).

At the base station 110, a transmit processor 220 may receive data, froma data source 212, intended for the UE 120 (or a set of UEs 120). Thetransmit processor 220 may select one or more modulation and codingschemes (MCSs) for the UE 120 based at least in part on one or morechannel quality indicators (CQIs) received from that UE 120. The basestation 110 may process (e.g., encode and modulate) the data for the UE120 based at least in part on the MCS(s) selected for the UE 120 and mayprovide data symbols for the UE 120. The transmit processor 220 mayprocess system information (e.g., for semi-static resource partitioninginformation (SRPI)) and control information (e.g., CQI requests, grants,and/or upper layer signaling) and provide overhead symbols and controlsymbols. The transmit processor 220 may generate reference symbols forreference signals (e.g., a cell-specific reference signal (CRS) or ademodulation reference signal (DMRS)) and synchronization signals (e.g.,a primary synchronization signal (PSS) or a secondary synchronizationsignal (SSS)). A transmit (TX) multiple-input multiple-output (MIMO)processor 230 may perform spatial processing (e.g., precoding) on thedata symbols, the control symbols, the overhead symbols, and/or thereference symbols, if applicable, and may provide a set of output symbolstreams (e.g., T output symbol streams) to a corresponding set of modems232 (e.g., T modems), shown as modems 232 a through 232 t. For example,each output symbol stream may be provided to a modulator component(shown as MOD) of a modem 232. Each modem 232 may use a respectivemodulator component to process a respective output symbol stream (e.g.,for OFDM) to obtain an output sample stream. Each modem 232 may furtheruse a respective modulator component to process (e.g., convert toanalog, amplify, filter, and/or upconvert) the output sample stream toobtain a downlink signal. The modems 232 a through 232 t may transmit aset of downlink signals (e.g., T downlink signals) via a correspondingset of antennas 234 (e.g., T antennas), shown as antennas 234 a through234 t.

At the UE 120, a set of antennas 252 (shown as antennas 252 a through252 r) may receive the downlink signals from the base station 110 and/orother base stations 110 and may provide a set of received signals (e.g.,R received signals) to a set of modems 254 (e.g., R modems), shown asmodems 254 a through 254 r. For example, each received signal may beprovided to a demodulator component (shown as DEMOD) of a modem 254.Each modem 254 may use a respective demodulator component to condition(e.g., filter, amplify, downconvert, and/or digitize) a received signalto obtain input samples. Each modem 254 may use a demodulator componentto further process the input samples (e.g., for OFDM) to obtain receivedsymbols. A MIMO detector 256 may obtain received symbols from the modems254, may perform MIMO detection on the received symbols if applicable,and may provide detected symbols. A receive processor 258 may process(e.g., demodulate and decode) the detected symbols, may provide decodeddata for the UE 120 to a data sink 260, and may provide decoded controlinformation and system information to a controller/processor 280. Theterm “controller/processor” may refer to one or more controllers, one ormore processors, or a combination thereof. A channel processor maydetermine a reference signal received power (RSRP) parameter, a receivedsignal strength indicator (RSSI) parameter, a reference signal receivedquality (RSRQ) parameter, and/or a CQI parameter, among other examples.In some examples, one or more components of the UE 120 may be includedin a housing 284.

The network controller 130 may include a communication unit 294, acontroller/processor 290, and a memory 292. The network controller 130may include, for example, one or more devices in a core network. Thenetwork controller 130 may communicate with the base station 110 via thecommunication unit 294.

One or more antennas (e.g., antennas 234 a through 234 t and/or antennas252 a through 252 r) may include, or may be included within, one or moreantenna panels, one or more antenna groups, one or more sets of antennaelements, and/or one or more antenna arrays, among other examples. Anantenna panel, an antenna group, a set of antenna elements, and/or anantenna array may include one or more antenna elements (within a singlehousing or multiple housings), a set of coplanar antenna elements, a setof non-coplanar antenna elements, and/or one or more antenna elementscoupled to one or more transmission and/or reception components, such asone or more components of FIG. 2 .

On the uplink, at the UE 120, a transmit processor 264 may receive andprocess data from a data source 262 and control information (e.g., forreports that include RSRP, RSSI, RSRQ, and/or CQI) from thecontroller/processor 280. The transmit processor 264 may generatereference symbols for one or more reference signals. The symbols fromthe transmit processor 264 may be precoded by a TX MIMO processor 266 ifapplicable, further processed by the modems 254 (e.g., for DFT-s-OFDM orCP-OFDM), and transmitted to the base station 110. In some examples, themodem 254 of the UE 120 may include a modulator and a demodulator. Insome examples, the UE 120 includes a transceiver. The transceiver mayinclude any combination of the antenna(s) 252, the modem(s) 254, theMIMO detector 256, the receive processor 258, the transmit processor264, and/or the TX MIMO processor 266. The transceiver may be used by aprocessor (e.g., the controller/processor 280) and the memory 282 toperform aspects of any of the methods described herein (e.g., withreference to FIGS. 4-9 ).

At the base station 110, the uplink signals from UE 120 and/or other UEsmay be received by the antennas 234, processed by the modem 232 (e.g., ademodulator component, shown as DEMOD, of the modem 232), detected by aMIMO detector 236 if applicable, and further processed by a receiveprocessor 238 to obtain decoded data and control information sent by theUE 120. The receive processor 238 may provide the decoded data to a datasink 239 and provide the decoded control information to thecontroller/processor 240. The base station 110 may include acommunication unit 244 and may communicate with the network controller130 via the communication unit 244. The base station 110 may include ascheduler 246 to schedule one or more UEs 120 for downlink and/or uplinkcommunications. In some examples, the modem 232 of the base station 110may include a modulator and a demodulator. In some examples, the basestation 110 includes a transceiver. The transceiver may include anycombination of the antenna(s) 234, the modem(s) 232, the MIMO detector236, the receive processor 238, the transmit processor 220, and/or theTX MIMO processor 230. The transceiver may be used by a processor (e.g.,the controller/processor 240) and the memory 242 to perform aspects ofany of the methods described herein (e.g., with reference to FIGS. 4-9).

The controller/processor 240 of the base station 110, thecontroller/processor 280 of the UE 120, and/or any other component(s) ofFIG. 2 may perform one or more techniques associated with transmittingand receiving quantized feedback in federated learning withrandomization, as described in more detail elsewhere herein. Forexample, the controller/processor 240 of the base station 110, thecontroller/processor 280 of the UE 120, and/or any other component(s) ofFIG. 2 may perform or direct operations of, for example, process 600 ofFIG. 6 , process 700 of FIG. 7 , and/or other processes as describedherein. The memory 242 and the memory 282 may store data and programcodes for the base station 110 and the UE 120, respectively. In someexamples, the memory 242 and/or the memory 282 may include anon-transitory computer-readable medium storing one or more instructions(e.g., code and/or program code) for wireless communication. Forexample, the one or more instructions, when executed (e.g., directly, orafter compiling, converting, and/or interpreting) by one or moreprocessors of the base station 110 and/or the UE 120, may cause the oneor more processors, the UE 120, and/or the base station 110 to performor direct operations of, for example, process 600 of FIG. 6 , process700 of FIG. 7 , and/or other processes as described herein. In someexamples, executing instructions may include running the instructions,converting the instructions, compiling the instructions, and/orinterpreting the instructions, among other examples. In some aspects,the server device described herein is the base station 110, is includedin the base station 110, or includes one or more components of the basestation 110 shown in FIG. 2 . In some aspects, the client devicedescribed herein is the UE 120, is included in the UE 120, or includesone or more components of the UE 120 shown in FIG. 2 .

In some aspects, a client device (e.g., UE 120, apparatus 800 of FIG. 8, and/or another client device, such as a tablet, a laptop, or a desktopcomputer, among other examples) may include means for determining afeedback associated with a machine learning component based at least inpart on applying the machine learning component; and/or means fortransmitting a quantized value based at least in part on the feedback,wherein the quantized value is determined based at least in part ondistances between the feedback and a non-uniform set of quantizeddigits. In some aspects, the means for the client device to performoperations described herein may include, for example, one or more ofantenna 252, modem 254, MIMO detector 256, receive processor 258,transmit processor 264, TX MIMO processor 266, controller/processor 280,or memory 282.

In some aspects, a server device (e.g., base station 110, apparatus 900of FIG. 9 , and/or another server device, such as one or more servercomputers in a server farm and/or at least a portion of a core networksupporting base station 110) may include means for transmitting, to aclient device (e.g., UE 120, apparatus 800 of FIG. 8 , and/or anotherclient device, such as a tablet, a laptop, or a desktop computer, amongother examples), a configuration associated with a machine learningcomponent, wherein the machine learning component accepts one or moreinputs to generate one or more outputs; and/or means for receiving aquantized value based at least in part on feedback from the clientdevice having applied the machine learning component, wherein thequantized value is based at least in part on distances between thefeedback and a non-uniform set of quantized digits. In some aspects, themeans for the server device to perform operations described herein mayinclude, for example, one or more of transmit processor 220, TX MIMOprocessor 230, modem 232, antenna 234, MIMO detector 236, receiveprocessor 238, controller/processor 240, memory 242, or scheduler 246.

While blocks in FIG. 2 are illustrated as distinct components, thefunctions described above with respect to the blocks may be implementedin a single hardware, software, or combination component or in variouscombinations of components. For example, the functions described withrespect to the transmit processor 264, the receive processor 258, and/orthe TX MIMO processor 266 may be performed by or under the control ofthe controller/processor 280.

As indicated above, FIG. 2 is provided as an example. Other examples maydiffer from what is described with regard to FIG. 2 .

FIG. 3 is a diagram illustrating an example 300 of federated learningfor machine learning components, in accordance with the presentdisclosure. As shown, a base station 110 may communicate with a set ofUEs 120 (shown as “UE 1, . . . UE k, . . . , and UE K”). The basestation 110 and the UEs 120 may communicate with one another via awireless network (e.g., the wireless network 100 shown in FIG. 1 ). Insome aspects, any number of additional UEs 120 may be included in theset of K UEs 120.

A machine learning component is a component (e.g., hardware, software,or a combination thereof) of a device (e.g., a client device, a serverdevice, a UE, a base station) that performs one or more machine learningprocedures. A machine learning component may include, for example,hardware and/or software that may learn to perform a procedure withoutbeing explicitly trained to perform the procedure. A machine learningcomponent may include, for example, a feature learning processing blockand/or a representation learning processing block. A machine learningcomponent may include one or more neural networks. A neural network mayinclude, for example, an autoencoder.

As shown in example 300, machine learning components may be trainedusing federated learning. Federated learning is a machine learningtechnique that enables multiple clients (e.g., UEs 120) tocollaboratively train machine learning models based on training data,while the server device (e.g., base station 110) does not collect thetraining data from the client devices. Federated learning techniques mayinvolve one or more global neural network models trained from datastored on multiple client devices (e.g., as described in further detailbelow).

As shown by reference number 310, the base station 110 may transmit amachine learning component to the UEs 120. As shown, the UEs 120 mayeach include a first communication manager 320. The first communicationmanager 320 may be configured to utilize the machine learning componentto perform one or more wireless communication tasks and/or one or moreuser interface tasks. The first communication manager 320 may beconfigured to utilize any number of additional machine learningcomponents.

As shown in FIG. 3 , the base station 110 may include a secondcommunication manager 330. The second communication manager 330 may beconfigured to utilize a global machine learning component to perform oneor more wireless communication tasks, to perform one or more userinterface tasks, and/or to facilitate federated learning associated withthe machine learning component.

The UEs 120 may each locally train the machine learning component usingtraining data collected by the UEs 120, respectively. Each UE 120 maytrain a machine learning component, such as a neural network, byoptimizing a set of model parameters (e.g., represented by w^((n)))associated with the machine learning component (where n represents afederated learning round index, as described below). The set of UEs 120may each be configured to provide updates to the base station 110multiple times (e.g., periodically, on demand, and/or upon updating alocal machine learning component).

A federated learning round refers to the training done by a UE 120 thatcorresponds to (e.g., precedes) an update provided by the UE 120 to thebase station 110. In some aspects, the federated learning round mayinclude the transmission by a UE 120, and the reception by the basestation 110, of an update. The federated learning round index (e.g.,represented by n) indicates the number of the rounds since the mostrecent global update was transmitted by the base station 110 to the UE120. The initial provisioning of a machine learning component on a UE120 and/or the transmission of a global update to the machine learningcomponent to a UE 120, among other examples, may trigger the beginningof a new round of federated learning.

In some aspects, for example, the first communication manager 320 of aUE 120 may determine an update corresponding to the machine learningcomponent by training the machine learning component. An update mayinclude any updated information, determined based at least in part on atraining procedure associated with the machine learning component. Anupdate may include, for example, an updated machine learning component(e.g., an updated neural network model), a set of updated parameters(e.g., a set of updated weights of a neural network), a set of gradientsassociated with a loss function of the machine learning component,and/or a compressed update, among other examples. In some aspects, asshown by reference number 340, each of the UEs 120 may collect trainingdata and store the training data in a memory device. The stored trainingdata may be referred to as a “local dataset.” As shown by referencenumber 350, each of the UEs 120 may determine a local update associatedwith the machine learning component.

In some aspects, for example, the first communication manager 320 mayaccess training data from the memory device and use the training data todetermine an input vector (e.g., represented by x_(j)) to be input intothe machine learning component to generate a training output (e.g.,represented by y_(j)) from the machine learning component. The inputvector x_(j) may include an array of input values, and the trainingoutput y_(j) may include a value (e.g., a value between 0 and 9).

The training output y_(j) may be used to facilitate determining themodel parameters w^((n)) that maximize a variational lower boundfunction. A negative variational lower bound function, which is thenegative of the variational lower bound function, may correspond to alocal loss function (e.g., represented by F_(k) (w)) which may beexpressed in a form similar to:

${{F_{k}(w)} = {\frac{1}{D_{k}}{\sum\limits_{{({x_{j},y_{j}})} \in D_{k}}{f\left( {w,x_{j},y_{j}} \right)}}}},$where D_(k) represents the size of the local dataset associated with theUE k. A stochastic gradient descent (SGD) algorithm may be used tooptimize the model parameters w^((n)). The first communication manager320 of a UE 120 may perform one or more SGD procedures to determine theoptimized parameters w^((n)) and may determine the gradients (e.g.,represented by g_(k) ^((n))=∇F_(k)(w^((n)))) of the loss function F(w).The first communication manager 320 may further refine the machinelearning component based at least in part on the loss function valueand/or the gradients, among other examples.

By repeating this process of training the machine learning component todetermine the gradients g_(k) ^((n)) a number of times, the firstcommunication manager 320 may determine an update corresponding to themachine learning component. Each repetition of the training proceduredescribed above may be referred to as an epoch. In some aspects, theupdate may include an updated set of model parameters w^((n)), adifference between the updated set of model parameters w^((n)) and aprior set of model parameters w^((n-1)), the set of gradients g_(k)^((n)), and/or an updated machine learning component (e.g., an updatedneural network model), among other examples.

As shown by reference number 360, the UEs 120 may each transmit theirrespective local updates (shown as “local update 1, . . . , local updatek, . . . , local update K”). In some aspects, a local update may includea compressed version of a local update. For example, in some aspects, aUE 120 may transmit a compressed set of gradients (e.g., represented by{tilde over (g)}_(k) ^((n))=q(g_(k) ^((n))), where q represents acompression scheme applied to the set of gradients g_(k) ^((n))).

As shown by reference number 370, the base station 110 (e.g., using thesecond communication manager 330) may aggregate the updates receivedfrom the UEs 120. For example, the second communication manager 330 mayaverage the received gradients to determine an aggregated update, whichmay be expressed in a form similar to:

${g^{(n)} = {\frac{1}{K}{\sum\limits_{k = 1}^{K}\;{\overset{\sim}{g}}_{k}^{(n)}}}},$where, as explained above, K represents the total quantity of UEs 120from which updates were received. In some examples, the secondcommunication manager 330 may aggregate the received updates using otheraggregation techniques. As shown by reference number 380, the secondcommunication manager 330 may update the global machine learningcomponent based on the aggregated updates. In some aspects, for example,the second communication manager 330 may update the global machinelearning component by normalizing the local datasets by treating eachdataset size (e.g., represented by D_(k)) as being equal. The secondcommunication manager 330 may update the global machine learningcomponent using multiple rounds of updates from the UEs 120 until aglobal loss function is minimized. The global loss function may begiven, for example, according to form similar to:

${{F(w)} = {\frac{\sum\limits_{k = 1}^{K}{\sum\limits_{j \in D_{k}}{f_{j}(w)}}}{K*D} = {\frac{1}{K}{\sum\limits_{k = 1}^{K}{F_{k}(w)}}}}},$where D_(k)=D, and where D represents a normalized constant. In someaspects, the base station 110 may transmit an update associated with theupdated global machine learning component to the UEs 120.

The UEs 120 may use the machine learning component for any number ofdifferent types of operations, transmissions, and/or user experienceenhancements, among other examples. In some aspects, the UEs 120 may useone or more machine learning components to report information to a basestation associated with received signals, user interactions with the UEs120, and/or positioning information, among other examples. In someaspects, the UEs 120 may perform measurements associated with referencesignals and use one or more machine learning component to facilitatereporting the measurements to a base station. For example, the UEs 120may measure reference signals during a beam management process forchannel state feedback (CSF), may measure received power of referencesignals from a serving cell and/or neighbor cells, may measure signalstrength of inter-radio access technology (e.g., WiFi) networks, and/ormay measure sensor signals for detecting locations of one or moreobjects within an environment, among other examples. In some aspects,the UEs 120 may use one or more machine learning components to use dataassociated with a user's interaction with the UEs 120 to customize orotherwise enhance a user experience with a user interface.

The exchange of information in this type of federated learning is oftendone over WiFi connections, where limited and/or costly communicationresources are not of concern due to wired connections associated withmodems, routers, and other hardware. However, implementing federatedlearning for machine learning components in the cellular context canenable positive impacts in network performance and user experience.

In some situations, in federated learning, a UE may use significantnetwork overhead and power to transmit an update to a base station.Accordingly, to reduce overhead, the UE may quantize the update andtransmit the quantized update to the base station. This quantization maybe applied to vectors (e.g., gradients g_(k) ^((n)) as described above)and/or scalars (e.g., updated weights for machine learning components).However, quantization often introduces a large error that increases as anumber of UEs used for the federated learning increases. For example, ifa plurality of UEs calculate a scalar of 0.75 as an update, and quantizethe scalar to 0.0 when quantizing to the nearest even digit, the basestation may receive a plurality of updates that indicate a quantizedscalar of 0.0 from the UEs, which has a relatively large error.

Some techniques and apparatuses described herein provide for moreaccurate quantization of updates for federated machine learning oflearning components. In some aspects, a client device (e.g., UE 120) maydetermine feedback using a machine learning component from a serverdevice (e.g., base station 110). For example, the UE 120 may locallytrain the machine learning component to determine a local updateassociated with the machine learning component. The UE 120 may quantizethe feedback using a non-uniform set of quantized digits. Accordingly,the base station 110 more accurately aggregates feedback from aplurality of UEs including the UE 120. For example, if a plurality ofUEs calculate a scalar of 0.75 as an update, the base station 110receives a plurality of updates that indicate a quantized scalar of 1.0from the UEs, when the UEs quantize to the nearest even digit or 1.0,which is non-uniform. As a result, network performance is improved byusing quantization during federated learning without incurringsignificant loss of accuracy during the federated learning.

As indicated above, FIG. 3 is provided merely as an example. Otherexamples may differ from what is described with regard to FIG. 3 .

FIG. 4 is a diagram illustrating an example 400 of non-uniformquantization, in accordance with the present disclosure. In example 400,a client device (such as a UE 120 and/or another client device, such astablet, a laptop, or a desktop computer) may apply a Lloyd algorithm toapply non-uniform quantization.

As shown in FIG. 4 , the client device may a determine a Voronoi diagram401 based at least in part on a set of points (e.g., updates fromfederated learning, as described in connection with FIG. 3 ), shown asdots 403 a, 403 b, 403 c, 403 d, and 403 e in FIG. 4 . The Voronoidiagram 401 may include a plurality of regions, where each regionincludes all points closer to a corresponding one of the set of pointsthan to any other of the set of points.

In some aspects, the client device may further determine a centroid ofeach region (shown as crosses 405 a, 405 b, 405 c, 405 d, and 405 e) andthen re-partition the space into regions to generate a new Voronoidiagram 407 based at least in part on the centroids. The client devicemay perform these determinations iteratively for a preconfigured numberof iterations and/or until the centroids satisfy one or more thresholds.For example, the client device may end the quantization when thecentroids from one iteration each individually or together collectivelyare within a threshold distance of the centroids from a next iteration.Although the description herein focuses on two-dimensional space, thedescription similarly applies to higher-ordered spaces, such asthree-dimensional space, four-dimensional space, and so on.

As indicated above, FIG. 4 is provided merely as an example. Otherexamples may differ from what is described with regard to FIG. 4 .

FIG. 5 is a diagram illustrating an example 500 of transmitting andreceiving non-uniform quantized feedback in federated learning, inaccordance with the present disclosure. In example 500, a UE 120 and abase station 110 may communicate with one another. In some aspects, theUE 120 and the base station 110 may communicate using a wirelessnetwork, such as wireless network 100 of FIG. 1 . Although thedescription below will focus on the UE 120 and the base station 110, thedescription similarly applies to other client devices (such as tablets,laptops, desktop computers, and/or other mobile or quasi-mobile devicesused for federated learning) and/or to other server devices (such as oneor more server computers on a server farm and/or at least a portion of acore network supporting the base station 110), respectively. Althoughthe description herein focuses on one UE 120, the description similarlyapplies to a plurality of UEs (e.g., UEs 120 of FIG. 3 , as describedabove).

As shown in connection with reference number 505, the base station 110may transmit, and the UE 120 may receive, a configuration associatedwith a machine learning component, where the machine learning componentaccepts one or more inputs to generate one or more outputs. In someaspects, the configuration may be a federated learning configuration.The configuration may be carried, for example, in a radio resourcecontrol (RRC) message. The configuration may indicate a machine learningcomponent that includes, for example, at least one neural network model.

As shown in connection with reference number 510, the UE 120 maydetermine a feedback, associated with the machine learning component,based at least in part on applying the machine learning component. Forexample, as described above in connection with FIG. 3 , the UE 120 mayaccess training data (e.g., stored in a memory of the UE 120, stored ina database accessible to the UE 120, and/or received from the basestation 110) and use the training data to determine an input vector(e.g., represented by x_(j)) to be input into the machine learningcomponent to generate a training output (e.g., represented by y_(j))from the machine learning component. The UE 120 may further use a localloss function (e.g., represented by F_(k) (w)) to determine the feedbackbased at least in part on the training output y_(j).

In some aspects, the feedback may include at least one scalar. Forexample, the feedback may include one or more updated weights for themachine learning component (e.g., associated with one or more nodes ofat least one neural network model and/or associated with one or morenodes of at least one decision tree).

Additionally, or alternatively, the feedback may include at least onevector. For example, as described above in connection with FIG. 3 , thefeedback may include one or more gradients (e.g., represented by g_(k)^((n))=∇F_(k)(w^((n)))) of the loss function F(w) (e.g., determinedusing an SGD algorithm to optimize the model parameters w^((n))).

As further shown in connection with reference number 510, the UE 120 maydetermine a quantized value based at least in part on distances betweenthe feedback and a non-uniform set of quantized digits. For example, theUE 120 may quantize the feedback in order to encode the feedback usingfewer bits, which reduces network overhead in transmitting the feedbackto the base station. The UE 120 may use non-uniform quantization suchthat the base station 110 may receive more accurate feedback from the UE120.

In one example, the UE 120 may select −5, −1, 1, and 5 as the quantizeddigits such that scalar feedback can be encoded using only two bits.Accordingly, the UE 120 may quantize feedback with a value of 0.6 as 1based at least in part on a distance of 0.4 between the value of thefeedback and the quantized digit of 1. In some aspects, the UE 120 mayuse non-uniform quantization with randomization. For example, the UE 120may select −4, −1, 1, and 4 as the quantized digits such that scalarfeedback can be encoded using only two bits. Accordingly, the UE 120 mayquantize feedback of 2.0 as 1 using a probability of 0.75 (or 75%) basedat least in part on a distance of 1.0 between the value of the feedbackand the quantized digit of 1, and as 4 using a probability of 0.25 (or25%), based at least in part on a distance of 2.0 between the value ofthe feedback and the quantized digit of 4.

In some aspects, the feedback may include a plurality of scalars.Accordingly, the quantized value may be based at least in part on all orsome of the plurality of scalars. For example, when the feedbackincludes a plurality of updated weights, the UE 120 may quantize one ofthe updated weights, more than one but not all of the updated weights,or all of the updated weights. In some aspects, the UE 120 may use thesame non-uniform set of quantized digits for two or more of theplurality of scalars. Additionally, or alternatively, the UE 120 may usedifferent sets of non-uniform quantized digits for two or more of theplurality of scalars.

In some aspects, the feedback may include at least one vector.Accordingly, the quantized value may be based at least in part on onecomponent of the at least one vector. As an alternative, the quantizedvalue may be based at least in part on two or more components of the atleast one vector. For example, the UE 120 may quantize some or allcomponents of the at least one vector. In some aspects, the UE 120 mayuse the same non-uniform set of quantized digits for two or more of thecomponents. Additionally, or alternatively, the UE 120 may use differentsets of non-uniform quantized digits for two or more of the components.

In some aspects, the quantized value may be based at least in part on aprojection of the at least one vector. For example, when the feedbackincludes at least one gradient (e.g., as described above), the UE 120may project the at least one gradient along one or more directions(e.g., using one or more unit vectors along those one or moredirections). In some aspects, when the feedback includes a plurality ofvectors, the UE 120 may project the plurality of vectors along the samedirection. As an alternative, the UE 120 may project at least some ofthe plurality of vectors along different directions.

In some aspects, the non-uniform set of quantized digits may be based atleast in part on a distribution of the feedback. For example, when thefeedback includes a plurality of scalars, the UE 120 may adjust thenon-uniform set of quantized digits based at least in part on adistribution of the scalars. Accordingly, in one example, the UE 120 mayselect −4, 1, 0, and 5 as the quantized digits when a cumulativedistribution function (CDF) associated with the feedbacks is 0.0 at −1;however, the UE 120 may select −4, −1, 0, and 5 as the quantized digitswhen a CDF associated with the feedbacks is 0.5 at −1. In anotherexample, when the feedback includes a plurality of vectors, the UE 120may adjust the non-uniform set of quantized digits based at least inpart on distributions of components of the vectors. For example, thenon-uniform set of quantized digits used to quantize a first componentmay depend, at least in part, on a distribution of first components ofthe vectors. Similarly, the non-uniform set of quantized digits used toquantize a second component may depend, at least in part, on adistribution of second components of the vectors. Although thedescription of this example focuses on vectors with two components, thedescription similarly applies to vectors with additional components,such as three components, four components, and so on. In some aspects,the non-uniform set of quantized digits may be based at least in part ona Lloyd algorithm (e.g., as described in connection with FIG. 3 ). Forexample, the UE 120 may determine the non-uniform set of quantizeddigits as centroids of a Voronoi diagram that is based at least in parton the feedback. Accordingly, the feedback may be quantized based atleast in part on the centroids. In some aspects, the UE 120 may applythe Lloyd algorithm once or iteratively (e.g., as described inconnection with FIG. 3 ). For example, the UE 120 determine a newnon-uniform set of quantized digits, at each iteration, as centroids ofa Voronoi diagram that is based at least in part on outputs from theprevious iteration.

In some aspects, the quantized value may be based at least in part onapplying uniform quantization after applying nonlinear companding usingthe non-uniform set of quantized digits. For example, the UE 120 mayapply a compressor that boosts some values of the feedback andattenuates other values of the feedback. Accordingly, the compressor mayuse the non-uniform set of quantized digits to determine when to boostand when to attenuate (e.g., in order to bring values closer tocorresponding non-uniform quantized digits). The UE 120 may thereafterapply uniform quantization to output from the compressor. In someaspects, the UE 120 may select a nonlinear companding method based atleast in part on a distribution of the feedback.

In some aspects, the base station 110 may transmit, and the UE 120 mayreceive, an indication of a formula and/or other algorithm to use fornon-uniform quantization. Although the description herein focuses on aformula, the description similarly applies to other types of algorithms.For example, the configuration described above in connection withreference number 505 may indicate the formula. Additionally, oralternatively, the base station 110 may transmit, and the UE 120 mayreceive, a separate message (e.g., via RRC signaling) indicating theformula.

In some aspects, the formula may accept values of the feedback as inputsand provide the non-uniform set of quantized digits as outputs. In someaspects, the formula may further accept a distribution of the feedbackas input.

In some aspects, the formula may be preconfigured. For example, theformula may be defined in 3GPP specifications and/or another standard.Accordingly, in some aspects, the UE 120 may be programmed (and/orotherwise preconfigured) with the formula. Additionally, oralternatively, the base station 110 may transmit an indication of theformula, from a plurality of preconfigured formulas, for the UE 120 touse. For example, the base station 110 may transmit one or more indicesindicating the formula, from a table of formulas in 3GPP specificationsand/or another standard, for the UE 120 to use.

Additionally, or alternatively, the base station 110 may transmit, andthe UE 120 may receive, an indication of the non-uniform set ofquantized digits. For example, the configuration described above inconnection with reference number 505 may indicate the non-uniform set ofquantized digits. Additionally, or alternatively, the base station 110may transmit, and the UE 120 may receive, a separate message (e.g., viaRRC signaling) indicating the non-uniform set of quantized digits.

In some aspects, the non-uniform set of quantized digits may bepreconfigured. For example, the non-uniform set of quantized digits maybe defined in 3GPP specifications and/or another standard. Accordingly,in some aspects, the UE 120 may be programmed (and/or otherwisepreconfigured) with the non-uniform set of quantized digits.Additionally, or alternatively, the base station 110 may transmit anindication of the non-uniform set of quantized digits, from a pluralityof preconfigured sets of non-uniform quantized digits, for the UE 120 touse. For example, the base station 110 may transmit one or more indicesindicating the non-uniform set of quantized digits, from a table of setsof non-uniform quantized digits in 3GPP specifications and/or anotherstandard, for the UE 120 to use.

Additionally, or alternatively, the base station 110 may transmit, andthe UE 120 may receive, an indication of a nonlinear companding methodto apply (e.g., as described above). For example, the configurationdescribed above in connection with reference number 505 may indicate thenonlinear companding method. Additionally, or alternatively, the basestation 110 may transmit, and the UE 120 may receive, a separate message(e.g., via RRC signaling) indicating the nonlinear companding method.

In some aspects, the nonlinear companding method may be preconfigured.For example, the nonlinear companding method may be defined in 3GPPspecifications and/or another standard. Accordingly, in some aspects,the UE 120 may be programmed (and/or otherwise preconfigured) with thenonlinear companding method. Additionally, or alternatively, the basestation 110 may transmit an indication of the nonlinear compandingmethod, from a plurality of preconfigured nonlinear companding methods,for the UE 120 to use. For example, the base station 110 may transmitone or more indices indicating the nonlinear companding method, from atable of nonlinear companding methods in 3GPP specifications and/oranother standard, for the UE 120 to use.

As shown in connection with reference number 515, the UE 120 maytransmit, and the base station 110 may receive, the quantized value. Thebase station 110 may determine an update based at least in part on thequantized value. In some aspects, the base station 110 may determine theupdate based at least in part on aggregating quantized values from aplurality of UEs (e.g., as described in connection with FIG. 3 ). Whenthe UE 120 uses nonlinear companding, the base station 110 may apply anexpander (e.g., an inverse of the compressor) to decode the feedbackfrom the quantized value.

Accordingly, the base station 110 may update the machine learningcomponent based at least in part on the update. In some aspects, thebase station 110 may determine a plurality of updates (e.g., based atleast in part on aggregating quantized values from multiple sets of UEs,where each set includes one or more UEs) and aggregate the plurality ofupdates to determine a global update for the machine learning component.In some aspects, example 500 may be recursive, where the base station110 re-transmits an updated machine learning component for additionaltraining by the UE 120 and/or other UEs in the federated learning.

By using techniques as described in connection with FIG. 5 , the UE 120quantizes the feedback based at least in part on distances between oneor more values of the feedback and a non-uniform set of quantizeddigits. Accordingly, the base station 110 more accurately aggregatesfeedback from a plurality of UEs including the UE 120. As a result, theUE 120 and the base station 110 experience lower network overhead andmemory overhead by using quantization during federated learning withoutincurring significant loss of accuracy during the federated learning.

As indicated above, FIG. 5 is provided merely as an example. Otherexamples may differ from what is described with regard to FIG. 5 .

FIG. 6 is a diagram illustrating an example process 600 performed, forexample, by a client device, in accordance with the present disclosure.Example process 600 is an example where the client device (e.g., UE 120,apparatus 800 of FIG. 8 , and/or another client device, such as atablet, a laptop, or a desktop computer) performs operations associatedwith transmitting non-uniform quantized feedback in federated learning.

As shown in FIG. 6 , in some aspects, process 600 may includedetermining a feedback associated with a machine learning componentbased at least in part on applying the machine learning component (block610). For example, the client device (e.g., using determinationcomponent 808, depicted in FIG. 8 ) may determine a feedback associatedwith a machine learning component based at least in part on applying themachine learning component, as described herein.

As further shown in FIG. 6 , in some aspects, process 600 may includetransmitting a quantized value based at least in part on the feedback(block 620). For example, the client device (e.g., using transmissioncomponent 804, depicted in FIG. 8 ) may transmit a quantized value basedat least in part on the feedback, as described herein. In some aspects,the quantized value is determined based at least in part on distancesbetween the feedback and a non-uniform set of quantized digits.

Process 600 may include additional aspects, such as any single aspect orany combination of aspects described below and/or in connection with oneor more other processes described elsewhere herein.

In a first aspect, the machine learning component comprises at least oneneural network.

In a second aspect, alone or in combination with the first aspect, thefeedback includes at least one weight.

In a third aspect, alone or in combination with one or more of the firstand second aspects, the feedback includes at least one vector.

In a fourth aspect, alone or in combination with one or more of thefirst through third aspects, the quantized value is based at least inpart on one component of the at least one vector.

In a fifth aspect, alone or in combination with one or more of the firstthrough fourth aspects, the quantized value is based at least in part ontwo or more components of the at least one vector.

In a sixth aspect, alone or in combination with one or more of the firstthrough fifth aspects, the quantized value is based at least in part ona projection of the at least one vector.

In a seventh aspect, alone or in combination with one or more of thefirst through sixth aspects, the quantized value is based at least inpart on applying uniform quantization after applying nonlinearcompanding using the non-uniform set of quantized digits.

In an eighth aspect, alone or in combination with one or more of thefirst through seventh aspects, the nonlinear companding is determinedbased at least in part on a distribution of the feedback.

In a ninth aspect, alone or in combination with one or more of the firstthrough eighth aspects, process 600 further includes receiving (e.g.,using reception component 802, depicted in FIG. 8 ) an indication of thenonlinear companding.

In a tenth aspect, alone or in combination with one or more of the firstthrough ninth aspects, the nonlinear companding is preconfigured.

In an eleventh aspect, alone or in combination with one or more of thefirst through tenth aspects, the non-uniform set of quantized digits isdetermined based at least in part on a distribution of the feedback.

In a twelfth aspect, alone or in combination with one or more of thefirst through eleventh aspects, the non-uniform set of quantized digitsis determined based at least in part on a Lloyd algorithm.

In a thirteenth aspect, alone or in combination with one or more of thefirst through twelfth aspects, process 600 further includes receiving(e.g., using reception component 802) an indication of the non-uniformset of quantized digits.

In a fourteenth aspect, alone or in combination with one or more of thefirst through thirteenth aspects, the non-uniform set of quantizeddigits is preconfigured.

Although FIG. 6 shows example blocks of process 600, in some aspects,process 600 may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in FIG. 6 .Additionally, or alternatively, two or more of the blocks of process 600may be performed in parallel.

FIG. 7 is a diagram illustrating an example process 700 performed, forexample, by a server device, in accordance with the present disclosure.Example process 700 is an example where the server device (e.g., basestation 110, apparatus 900 of FIG. 9 , and/or another service device,such as one or more server computers in a server farm and/or at least aportion of a core network supporting base station 110) performsoperations associated with receiving non-uniform quantized feedback infederated learning.

As shown in FIG. 7 , in some aspects, process 700 may includetransmitting, to a client device (e.g., UE 120, apparatus 800 of FIG. 8, and/or another client device, such as a tablet, a laptop, or a desktopcomputer), a configuration associated with a machine learning component(block 710). For example, the server device (e.g., using transmissioncomponent 904, depicted in FIG. 9 ) may transmit, to a client device, aconfiguration associated with a machine learning component, as describedherein. In some aspects, the machine learning component accepts one ormore inputs to generate one or more outputs.

As further shown in FIG. 7 , in some aspects, process 700 may includereceiving a quantized value based at least in part on feedback from theclient device having applied the machine learning component (block 720).For example, the server device (e.g., using reception component 902,depicted in FIG. 9 ) may receive a quantized value based at least inpart on feedback from the client device having applied the machinelearning component, as described herein. In some aspects, the quantizedvalue is based at least in part on distances between the feedback and anon-uniform set of quantized digits.

Process 700 may include additional aspects, such as any single aspect orany combination of aspects described below and/or in connection with oneor more other processes described elsewhere herein.

In a first aspect, the machine learning component comprises at least oneneural network.

In a second aspect, alone or in combination with the first aspect, thefeedback includes at least one weight.

In a third aspect, alone or in combination with one or more of the firstand second aspects, the feedback includes at least one vector.

In a fourth aspect, alone or in combination with one or more of thefirst through third aspects, the quantized value is based at least inpart on one component of the at least one vector.

In a fifth aspect, alone or in combination with one or more of the firstthrough fourth aspects, the quantized value is based at least in part ontwo or more components of the at least one vector.

In a sixth aspect, alone or in combination with one or more of the firstthrough fifth aspects, the quantized value is based at least in part ona projection of the at least one vector.

In a seventh aspect, alone or in combination with one or more of thefirst through sixth aspects, the quantized value is based at least inpart on applying uniform quantization after applying nonlinearcompanding using the non-uniform set of quantized digits.

In an eighth aspect, alone or in combination with one or more of thefirst through seventh aspects, the nonlinear companding is determinedbased at least in part on a distribution of the feedback.

In a ninth aspect, alone or in combination with one or more of the firstthrough eighth aspects, process 700 further includes transmitting (e.g.,using transmission component 904, depicted in FIG. 9 ) an indication ofthe nonlinear companding.

In a tenth aspect, alone or in combination with one or more of the firstthrough ninth aspects, the nonlinear companding is preconfigured.

In an eleventh aspect, alone or in combination with one or more of thefirst through tenth aspects, the non-uniform set of quantized digits isdetermined based at least in part on a distribution of the feedback.

In a twelfth aspect, alone or in combination with one or more of thefirst through eleventh aspects, the non-uniform set of quantized digitsis determined based at least in part on a Lloyd algorithm.

In a thirteenth aspect, alone or in combination with one or more of thefirst through twelfth aspects, process 700 further includes transmitting(e.g., using transmission component 904) an indication of thenon-uniform set of quantized digits.

In a fourteenth aspect, alone or in combination with one or more of thefirst through thirteenth aspects, the non-uniform set of quantizeddigits is preconfigured.

Although FIG. 7 shows example blocks of process 700, in some aspects,process 700 may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in FIG. 7 .Additionally, or alternatively, two or more of the blocks of process 700may be performed in parallel.

FIG. 8 is a block diagram of an example apparatus 800 for wirelesscommunication. The apparatus 800 may be a client device, or a clientdevice may include the apparatus 800. In some aspects, the apparatus 800includes a reception component 802 and a transmission component 804,which may be in communication with one another (for example, via one ormore buses and/or one or more other components). As shown, the apparatus800 may communicate with another apparatus 806 (such as a UE, a basestation, or another wireless communication device) using the receptioncomponent 802 and the transmission component 804. As further shown, theapparatus 800 may include a determination component 808, among otherexamples.

In some aspects, the apparatus 800 may be configured to perform one ormore operations described herein in connection with FIGS. 4-5 .Additionally, or alternatively, the apparatus 800 may be configured toperform one or more processes described herein, such as process 600 ofFIG. 6 , or a combination thereof. In some aspects, the apparatus 800and/or one or more components shown in FIG. 8 may include one or morecomponents of the UE described above in connection with FIG. 2 .Additionally, or alternatively, one or more components shown in FIG. 8may be implemented within one or more components described above inconnection with FIG. 2 . Additionally, or alternatively, one or morecomponents of the set of components may be implemented at least in partas software stored in a memory. For example, a component (or a portionof a component) may be implemented as instructions or code stored in anon-transitory computer-readable medium and executable by a controlleror a processor to perform the functions or operations of the component.

The reception component 802 may receive communications, such asreference signals, control information, data communications, or acombination thereof, from the apparatus 806. The reception component 802may provide received communications to one or more other components ofthe apparatus 800. In some aspects, the reception component 802 mayperform signal processing on the received communications (such asfiltering, amplification, demodulation, analog-to-digital conversion,demultiplexing, deinterleaving, de-mapping, equalization, interferencecancellation, or decoding, among other examples), and may provide theprocessed signals to the one or more other components of the apparatus800. In some aspects, the reception component 802 may include one ormore antennas, a demodulator, a MIMO detector, a receive processor, acontroller/processor, a memory, or a combination thereof, of the UEdescribed above in connection with FIG. 2 .

The transmission component 804 may transmit communications, such asreference signals, control information, data communications, or acombination thereof, to the apparatus 806. In some aspects, one or moreother components of the apparatus 800 may generate communications andmay provide the generated communications to the transmission component804 for transmission to the apparatus 806. In some aspects, thetransmission component 804 may perform signal processing on thegenerated communications (such as filtering, amplification, modulation,digital-to-analog conversion, multiplexing, interleaving, mapping, orencoding, among other examples), and may transmit the processed signalsto the apparatus 806. In some aspects, the transmission component 804may include one or more antennas, a modulator, a transmit MIMOprocessor, a transmit processor, a controller/processor, a memory, or acombination thereof, of the UE described above in connection with FIG. 2. In some aspects, the transmission component 804 may be co-located withthe reception component 802 in a transceiver.

In some aspects, the determination component 808 may determine afeedback associated with a machine learning component based at least inpart on applying the machine learning component. In some aspects, thedetermination component 808 may include a receive processor, a transmitprocessor, a controller/processor, a memory, or a combination thereof,of the UE described above in connection with FIG. 2 . The transmissioncomponent 804 may transmit (e.g., to a server device, such as theapparatus 806) a quantized value based at least in part on the feedback.In some aspects, the determination component 808 may determine thequantized value based at least in part on distances between the feedbackand a non-uniform set of quantized digits.

In some aspects, the reception component 802 may receive (e.g., from theapparatus 806) an indication of a nonlinear companding that thedetermination component 808 uses to determine the quantized value.Additionally, or alternatively, the reception component 802 may receive(e.g., from the apparatus 806) an indication of the non-uniform set ofquantized digits.

The number and arrangement of components shown in FIG. 8 are provided asan example. In practice, there may be additional components, fewercomponents, different components, or differently arranged componentsthan those shown in FIG. 8 . Furthermore, two or more components shownin FIG. 8 may be implemented within a single component, or a singlecomponent shown in FIG. 8 may be implemented as multiple, distributedcomponents. Additionally, or alternatively, a set of (one or more)components shown in FIG. 8 may perform one or more functions describedas being performed by another set of components shown in FIG. 8 .

FIG. 9 is a block diagram of an example apparatus 900 for wirelesscommunication. The apparatus 900 may be a server device, or a serverdevice may include the apparatus 900. In some aspects, the apparatus 900includes a reception component 902 and a transmission component 904,which may be in communication with one another (for example, via one ormore buses and/or one or more other components). As shown, the apparatus900 may communicate with another apparatus 906 (such as a UE, a basestation, or another wireless communication device) using the receptioncomponent 902 and the transmission component 904. As further shown, theapparatus 900 may include a quantization component 908, among otherexamples.

In some aspects, the apparatus 900 may be configured to perform one ormore operations described herein in connection with FIGS. 4-5 .Additionally, or alternatively, the apparatus 900 may be configured toperform one or more processes described herein, such as process 700 ofFIG. 7 , or a combination thereof. In some aspects, the apparatus 900and/or one or more components shown in FIG. 9 may include one or morecomponents of the base station described above in connection with FIG. 2. Additionally, or alternatively, one or more components shown in FIG. 9may be implemented within one or more components described above inconnection with FIG. 2 . Additionally, or alternatively, one or morecomponents of the set of components may be implemented at least in partas software stored in a memory. For example, a component (or a portionof a component) may be implemented as instructions or code stored in anon-transitory computer-readable medium and executable by a controlleror a processor to perform the functions or operations of the component.

The reception component 902 may receive communications, such asreference signals, control information, data communications, or acombination thereof, from the apparatus 906. The reception component 902may provide received communications to one or more other components ofthe apparatus 900. In some aspects, the reception component 902 mayperform signal processing on the received communications (such asfiltering, amplification, demodulation, analog-to-digital conversion,demultiplexing, deinterleaving, de-mapping, equalization, interferencecancellation, or decoding, among other examples), and may provide theprocessed signals to the one or more other components of the apparatus900. In some aspects, the reception component 902 may include one ormore antennas, a demodulator, a MIMO detector, a receive processor, acontroller/processor, a memory, or a combination thereof, of the basestation described above in connection with FIG. 2 .

The transmission component 904 may transmit communications, such asreference signals, control information, data communications, or acombination thereof, to the apparatus 906. In some aspects, one or moreother components of the apparatus 900 may generate communications andmay provide the generated communications to the transmission component904 for transmission to the apparatus 906. In some aspects, thetransmission component 904 may perform signal processing on thegenerated communications (such as filtering, amplification, modulation,digital-to-analog conversion, multiplexing, interleaving, mapping, orencoding, among other examples), and may transmit the processed signalsto the apparatus 906. In some aspects, the transmission component 904may include one or more antennas, a modulator, a transmit MIMOprocessor, a transmit processor, a controller/processor, a memory, or acombination thereof, of the base station described above in connectionwith FIG. 2 . In some aspects, the transmission component 904 may beco-located with the reception component 902 in a transceiver.

In some aspects, the transmission component 904 may transmit (e.g., to aclient device, such as the apparatus 906) a configuration associatedwith a machine learning component that accepts one or more inputs togenerate one or more outputs. Accordingly, the reception component 902may receive a quantized value based at least in part on feedback fromthe apparatus 906 having applied the machine learning component. In someaspects, the quantized value may be based at least in part on distancesbetween the feedback and a non-uniform set of quantized digits.

In some aspects, the transmission component 904 may transmit (e.g., tothe apparatus 906) an indication of a nonlinear companding that theapparatus 906 may use to determine the quantized value. For example, thequantization component 908 may determine the nonlinear companding (e.g.,based at least in part on an expected distribution of the feedback). Insome aspects, the quantization component 908 may include a receiveprocessor, a transmit processor, a controller/processor, a memory, or acombination thereof, of the base station described above in connectionwith FIG. 2 . Additionally, or alternatively, the nonlinear compandingmay be preconfigured.

Additionally, or alternatively, the transmission component 904 maytransmit (e.g., to the apparatus 906) an indication of the non-uniformset of quantized digits. For example, the quantization component 908 maydetermine the non-uniform set of quantized digits (e.g., based at leastin part on an expected distribution of the feedback). Additionally, oralternatively, the non-uniform set of quantized digits may bepreconfigured.

The number and arrangement of components shown in FIG. 9 are provided asan example. In practice, there may be additional components, fewercomponents, different components, or differently arranged componentsthan those shown in FIG. 9 . Furthermore, two or more components shownin FIG. 9 may be implemented within a single component, or a singlecomponent shown in FIG. 9 may be implemented as multiple, distributedcomponents. Additionally, or alternatively, a set of (one or more)components shown in FIG. 9 may perform one or more functions describedas being performed by another set of components shown in FIG. 9 .

The following provides an overview of some Aspects of the presentdisclosure:

Aspect 1: A method of wireless communication performed by a clientdevice, comprising: determining a feedback associated with a machinelearning component based at least in part on applying the machinelearning component; and transmitting a quantized value based at least inpart on the feedback, wherein the quantized value is determined based atleast in part on distances between the feedback and a non-uniform set ofquantized digits.

Aspect 2: The method of Aspect 1, wherein the machine learning componentcomprises at least one neural network.

Aspect 3: The method of any of Aspects 1 through 2, wherein the feedbackincludes at least one weight.

Aspect 4: The method of any of Aspects 1 through 3, wherein the feedbackincludes at least one vector.

Aspect 5: The method of Aspect 4, wherein the quantized value is basedat least in part on one component of the at least one vector.

Aspect 6: The method of Aspect 4, wherein the quantized value is basedat least in part on two or more components of the at least one vector.

Aspect 7: The method of any of Aspects 4 through 6, wherein thequantized value is based at least in part on a projection of the atleast one vector.

Aspect 8: The method of any of Aspects 1 through 7, wherein thequantized value is based at least in part on applying uniformquantization after applying nonlinear companding using the non-uniformset of quantized digits.

Aspect 9: The method of Aspect 8, wherein the nonlinear companding isdetermined based at least in part on a distribution of the feedback.

Aspect 10: The method of any of Aspects 8 through 9, further comprising:receiving an indication of the nonlinear companding.

Aspect 11: The method of any of Aspects 8 through 10, wherein thenonlinear companding is preconfigured.

Aspect 12: The method of any of Aspects 1 through 11, wherein thenon-uniform set of quantized digits is determined based at least in parton a distribution of the feedback.

Aspect 13: The method of any of Aspects 1 through 12, wherein thenon-uniform set of quantized digits is determined based at least in parton a Lloyd algorithm.

Aspect 14: The method of any of Aspects 1 through 13, furthercomprising: receiving an indication of the non-uniform set of quantizeddigits.

Aspect 15: The method of any of Aspects 1 through 14, wherein thenon-uniform set of quantized digits is preconfigured.

Aspect 16: A method of wireless communication performed by a serverdevice, comprising: transmitting, to a client device, a configurationassociated with a machine learning component, wherein the machinelearning component accepts one or more inputs to generate one or moreoutputs; and receiving a quantized value based at least in part onfeedback from the client device having applied the machine learningcomponent, wherein the quantized value is based at least in part ondistances between the feedback and a non-uniform set of quantizeddigits.

Aspect 17: The method of Aspect 16, wherein the machine learningcomponent comprises at least one neural network.

Aspect 18: The method of any of Aspects 16 through 17, wherein thefeedback includes at least one weight.

Aspect 19: The method of any of Aspects 16 through 18, wherein thefeedback includes at least one vector.

Aspect 20: The method of Aspect 19, wherein the quantized value is basedat least in part on one component of the at least one vector.

Aspect 21: The method of Aspect 19, wherein the quantized value is basedat least in part on two or more components of the at least one vector.

Aspect 22: The method of any of Aspects 19 through 21, wherein thequantized value is based at least in part on a projection of the atleast one vector.

Aspect 23: The method of any of Aspects 16 through 22, wherein thequantized value is based at least in part on applying uniformquantization after applying nonlinear companding using the non-uniformset of quantized digits.

Aspect 24: The method of Aspect 23, wherein the nonlinear companding isdetermined based at least in part on a distribution of the feedback.

Aspect 25: The method of any of Aspects 23 through 24, furthercomprising: transmitting an indication of the nonlinear companding.

Aspect 26: The method of any of Aspects 23 through 25, wherein thenonlinear companding is preconfigured.

Aspect 27: The method of any of Aspects 16 through 26, wherein thenon-uniform set of quantized digits is determined based at least in parton a distribution of the feedback.

Aspect 28: The method of any of Aspects 16 through 27, wherein thenon-uniform set of quantized digits is determined based at least in parton a Lloyd algorithm.

Aspect 29: The method of any of Aspects 16 through 28, furthercomprising: transmitting an indication of the non-uniform set ofquantized digits.

Aspect 30: The method of any of Aspects 16 through 29, wherein thenon-uniform set of quantized digits is preconfigured.

Aspect 31: An apparatus for wireless communication at a device,comprising a processor; memory coupled with the processor; andinstructions stored in the memory and executable by the processor tocause the apparatus to perform the method of one or more of Aspects1-15.

Aspect 32: A device for wireless communication, comprising a memory andone or more processors coupled to the memory, the one or more processorsconfigured to perform the method of one or more of Aspects 1-15.

Aspect 33: An apparatus for wireless communication, comprising at leastone means for performing the method of one or more of Aspects 1-15.

Aspect 34: A non-transitory computer-readable medium storing code forwireless communication, the code comprising instructions executable by aprocessor to perform the method of one or more of Aspects 1-15.

Aspect 35: A non-transitory computer-readable medium storing a set ofinstructions for wireless communication, the set of instructionscomprising one or more instructions that, when executed by one or moreprocessors of a device, cause the device to perform the method of one ormore of Aspects 1-15.

Aspect 36: An apparatus for wireless communication at a device,comprising a processor; memory coupled with the processor; andinstructions stored in the memory and executable by the processor tocause the apparatus to perform the method of one or more of Aspects16-30.

Aspect 37: A device for wireless communication, comprising a memory andone or more processors coupled to the memory, the one or more processorsconfigured to perform the method of one or more of Aspects 16-30.

Aspect 38: An apparatus for wireless communication, comprising at leastone means for performing the method of one or more of Aspects 16-30.

Aspect 39: A non-transitory computer-readable medium storing code forwireless communication, the code comprising instructions executable by aprocessor to perform the method of one or more of Aspects 16-30.

Aspect 40: A non-transitory computer-readable medium storing a set ofinstructions for wireless communication, the set of instructionscomprising one or more instructions that, when executed by one or moreprocessors of a device, cause the device to perform the method of one ormore of Aspects 16-30.

The foregoing disclosure provides illustration and description but isnot intended to be exhaustive or to limit the aspects to the preciseforms disclosed. Modifications and variations may be made in light ofthe above disclosure or may be acquired from practice of the aspects.

As used herein, the term “component” is intended to be broadly construedas hardware and/or a combination of hardware and software. “Software”shall be construed broadly to mean instructions, instruction sets, code,code segments, program code, programs, subprograms, software modules,applications, software applications, software packages, routines,subroutines, objects, executables, threads of execution, procedures,and/or functions, among other examples, whether referred to as software,firmware, middleware, microcode, hardware description language, orotherwise. As used herein, a “processor” is implemented in hardwareand/or a combination of hardware and software. It will be apparent thatsystems and/or methods described herein may be implemented in differentforms of hardware and/or a combination of hardware and software. Theactual specialized control hardware or software code used to implementthese systems and/or methods is not limiting of the aspects. Thus, theoperation and behavior of the systems and/or methods are describedherein without reference to specific software code, since those skilledin the art will understand that software and hardware can be designed toimplement the systems and/or methods based, at least in part, on thedescription herein.

As used herein, “satisfying a threshold” may, depending on the context,refer to a value being greater than the threshold, greater than or equalto the threshold, less than the threshold, less than or equal to thethreshold, equal to the threshold, not equal to the threshold, or thelike.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various aspects. Many of thesefeatures may be combined in ways not specifically recited in the claimsand/or disclosed in the specification. The disclosure of various aspectsincludes each dependent claim in combination with every other claim inthe claim set. As used herein, a phrase referring to “at least one of” alist of items refers to any combination of those items, including singlemembers. As an example, “at least one of: a, b, or c” is intended tocover a, b, c, a+b, a+c, b+c, and a+b+c, as well as any combination withmultiples of the same element (e.g., a+a, a+a+a, a+a+b, a+a+c, a+b+b,a+c+c, b+b, b+b+b, b+b+c, c+c, and c+c+c, or any other ordering of a, b,and c).

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterms “set” and “group” are intended to include one or more items andmay be used interchangeably with “one or more.” Where only one item isintended, the phrase “only one” or similar language is used. Also, asused herein, the terms “has,” “have,” “having,” or the like are intendedto be open-ended terms that do not limit an element that they modify(e.g., an element “having” A may also have B). Further, the phrase“based on” is intended to mean “based, at least in part, on” unlessexplicitly stated otherwise. Also, as used herein, the term “or” isintended to be inclusive when used in a series and may be usedinterchangeably with “and/or,” unless explicitly stated otherwise (e.g.,if used in combination with “either” or “only one of”).

What is claimed is:
 1. An apparatus for wireless communication at aclient device, the apparatus comprising: a memory; and one or moreprocessors, coupled to the memory, configured to: determine a feedbackassociated with a machine learning component based at least in part onapplying the machine learning component; and transmit a quantized valuebased at least in part on the feedback, wherein the quantized value isdetermined based at least in part on distances between the feedback anda non-uniform set of quantized digits and based at least in part onapplying nonlinear companding utilizing the non-uniform set of quantizeddigits.
 2. The apparatus of claim 1, wherein the machine learningcomponent comprises at least one neural network.
 3. The apparatus ofclaim 1, wherein the feedback includes at least one weight.
 4. Theapparatus of claim 1, wherein the feedback includes at least one vector.5. The apparatus of claim 4, wherein the quantized value is based atleast in part on one component of the at least one vector.
 6. Theapparatus of claim 4, wherein the quantized value is based at least inpart on two or more components of the at least one vector.
 7. Theapparatus of claim 4, wherein the quantized value is based at least inpart on a projection of the at least one vector.
 8. The apparatus ofclaim 1, wherein the quantized value is based at least in part onapplying uniform quantization after applying the nonlinear compandingusing the non-uniform set of quantized digits.
 9. The apparatus of claim1, wherein the nonlinear companding is determined based at least in parton a distribution of the feedback.
 10. The apparatus of claim 1, whereinthe one or more processors are further configured to: receive anindication of the nonlinear companding.
 11. The apparatus of claim 1,wherein the nonlinear companding is preconfigured.
 12. The apparatus ofclaim 1, wherein the non-uniform set of quantized digits is determinedbased at least in part on a distribution of the feedback.
 13. Theapparatus of claim 1, wherein the non-uniform set of quantized digits isdetermined based at least in part on a Lloyd algorithm.
 14. Theapparatus of claim 1, wherein the one or more processors are furtherconfigured to: receive an indication of the non-uniform set of quantizeddigits.
 15. The apparatus of claim 1, wherein the non-uniform set ofquantized digits is preconfigured.
 16. An apparatus for wirelesscommunication at a server device, the apparatus comprising: a memory;and one or more processors, coupled to the memory, configured to:transmit, to a client device, a configuration associated with a machinelearning component, wherein the machine learning component accepts oneor more inputs to generate one or more outputs; and receive a quantizedvalue based at least in part on feedback from the client device havingapplied the machine learning component, wherein the quantized value isdetermined based at least in part on distances between the feedback anda non-uniform set of quantized digits and based at least in part onapplying nonlinear companding utilizing the non-uniform set of quantizeddigits.
 17. The apparatus of claim 16, wherein the machine learningcomponent comprises at least one neural network.
 18. The apparatus ofclaim 16, wherein the feedback includes at least one weight.
 19. Theapparatus of claim 16, wherein the feedback includes at least onevector.
 20. The apparatus of claim 19, wherein the quantized value isbased at least in part on one component of the at least one vector. 21.The apparatus of claim 19, wherein the quantized value is based at leastin part on two or more components of the at least one vector.
 22. Theapparatus of claim 19, wherein the quantized value is based at least inpart on a projection of the at least one vector.
 23. The apparatus ofclaim 16, wherein the quantized value is based at least in part onapplying uniform quantization after applying the nonlinear compandingusing the non-uniform set of quantized digits.
 24. The apparatus ofclaim 16, wherein the nonlinear companding is determined based at leastin part on a distribution of the feedback.
 25. The apparatus of claim16, wherein the one or more processors are further configured to:transmit an indication of the nonlinear companding.
 26. The apparatus ofclaim 16, wherein the non-uniform set of quantized digits is determinedbased at least in part on a distribution of the feedback.
 27. Theapparatus of claim 16, wherein the non-uniform set of quantized digitsis determined based at least in part on a Lloyd algorithm.
 28. Theapparatus of claim 16, wherein the one or more processors are furtherconfigured to: transmit an indication of the non-uniform set ofquantized digits.
 29. A method of wireless communication performed by aclient device, the method comprising: determining a feedback associatedwith a machine learning component based at least in part on applying themachine learning component; and transmitting a quantized value based atleast in part on the feedback, wherein the quantized value is determinedbased at least in part on distances between the feedback and anon-uniform set of quantized digits and based at least in part onapplying nonlinear companding utilizing the non-uniform set of quantizeddigits.
 30. A method of wireless communication performed by a serverdevice, the method comprising: transmitting, to a client device, aconfiguration associated with a machine learning component, wherein themachine learning component accepts one or more inputs to generate one ormore outputs; and receiving a quantized value based at least in part onfeedback from the client device having applied the machine learningcomponent, wherein the quantized value is based at least in part ondistances between the feedback and a non-uniform set of quantized digitsand based at least in part on applying nonlinear companding utilizingthe non-uniform set of quantized digits.